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Questions tagged [machine-learning]

Theoretical questions about Machine learning, especially Computational Learning Theory, including Algorithmic Learning Theory, PAC learning, and Bayesian Inference

4
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0answers
105 views

Learning hidden variable distribution

Consider a set of $k$ continuous variables. Each variable $x_k$ is associated with a hidden distribution from which its value is sampled independently of other variables. I am given a set of ...
7
votes
0answers
153 views

Universal approximation theorem of second order

The universal approximation theorem (https://en.wikipedia.org/wiki/Universal_approximation_theorem) informally states that up to several conditions, any function can be approximated by a shallow ...
-1
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1answer
94 views

What is the name of the category of problems that can only be solved with machine learning? [closed]

Wikipedia defines machine learning as the "field of computer science that gives computers the ability to learn without being explicitly programmed". A common example of a problem which machine ...
3
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0answers
61 views

Is there some research about infinitely many-armed bandit with non-stationary assumption?

Is there some research about infinitely many-armed bandit with non-stationary assumption? I have found the paper about infinitely many-armed bandit under stationary (or stochastic) assumption. And I ...
2
votes
2answers
124 views

Can machine learning algorithms be trained to discard nonsense?

Afaik, the problem with many machine learning algorithms is that they will often label nonsense into some categories. What measures can one take to discard nonsense results? Eg. if you have a bot ...
1
vote
1answer
80 views

References for the computational complexity of training neural networks

I'm looking for a good review paper or book chapter that offers an accessible introduction to the computational complexity of training neural networks for classification problems. In particular, I'm ...
9
votes
5answers
648 views

Can neural networks be used to devise algorithms?

After the newer and newer successes of neural networks in playing board games, one feels that the next goal we set could be something more useful than beating humans in Starcraft. More precisely, I ...
0
votes
1answer
62 views

Learning a discrete distribution in $\ell_r$ norm

Let $P=(p_1,\ldots,p_d)$ be a distribution on $[d]$. Given $n$ iid draws from $P$, we construct some empirical estimate $\hat P_n=(\hat p_{n,1},\ldots,\hat p_{n,d})$. Let us define the $r$-risk by $$ ...
5
votes
1answer
482 views

Understanding the No Free Lunch Theorem

I came across the No Free Lunch Theorem via Jürgen Schmidhuber's paper on Universal Search and there were a couple remarks on NFL which stood out to me. The first was that we can't define a uniform ...
1
vote
1answer
133 views

$L_\mathcal{D}(A(S)) \le 0.1$ with prob at least $0.9$ implies PAC learnability

Suppose we have a hypothesis class $\mathcal{H}$ that is non-uniform learnable via sample compelxity function $m_{\text{NUL}}:[0,1]^2 \times \mathcal{H} \rightarrow \mathbb{N}$. If we define $\mathcal{...
13
votes
0answers
215 views

Differential privacy and data poisoning

A differentially private algorithm takes datasets containing inputs and produces randomized outputs, such that no small change in the dataset can shift the distribution of outputs by too much. This ...
4
votes
1answer
560 views

Autoencoders and information compression

Disclaimer: I know very (very) little about deep nets, besides what an introductory course on machine learning would teach on neural networks, and skimming some paper abstracts and introductions. If ...
16
votes
1answer
751 views

Is BPP vs. P a real problem after we know BPP lies in P/poly?

We know (for now about 40 years, thank Adleman, Bennet and Gill) that the inclusion BPP $\subseteq$ P/poly, and an even stronger BPP/poly $\subseteq$ P/poly hold. The "/poly" means that we work non-...
1
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0answers
183 views

Convergence of Q-learning with non-linear function approximation

Q-learning is a well-known algorithm in Reinforcement learning which enjoys great empirical success but with insufficient theoretical understanding. In the tabular setting, it is known that if each ...
28
votes
1answer
1k views

Functions that are Not Efficiently Computable but Learnable

We know that (see, e.g., Theorems 1 and 3 of [1]), roughly speaking, under suitable conditions, functions that can be efficiently computed by Turing machine in polynomial time ("efficiently computable"...
3
votes
2answers
406 views

Learning a coin's bias (localized)

It's well known that the minimax sample complexity for estimating the bias $p$ of a coin to additive error $\epsilon$ with confidence $\delta$ is $\Theta(\epsilon^{-2}\log(1/\delta))$. What if we ...
4
votes
1answer
215 views

Adversarial Machine Learning, Learning with (Malicious) noise

I am reading some old papers regarding Learning With Malicious Noise. In one of them, Learning in the presence of Malicious Errors, by Kearns and Li $[1]$ (https://www.cis.upenn.edu/~mkearns/papers/...
6
votes
1answer
360 views

Applications of Takens' theorem to TCS?

My apologies if the question is a tad vague—I did try to search the literature for more, but didn't find anything (the similarity between the keywords "Takens" and "taken" on Google may be partly to ...
4
votes
1answer
234 views

Textbook/resources for a beginning researcher in (Machine) Learning Theory

I'm looking to begin understanding basic concepts, notions, results and definitions in the area of Computational Learning Theory (or the theory of Machine Learning), as is done in the theoretical ...
1
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0answers
121 views

Boolean functions with high query complexity for PAC learning

The most general theorem for PAC learning of Boolean functions that I am aware of is the theorem in section 3.4 of Ryan O'Donnel's book where its basically shown that Boolean functions whose Fourier ...
4
votes
0answers
1k views

Universal Approximation Theorem for non-sigmoidal activation functions

The most cited Universal Approximation Theories for multi-layer feedforward neural networks by Cybenko (1989) and Hornik (1991) assume the activation functions of the network to be sigmoidal. However, ...
1
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0answers
78 views

Off-policy Monte Carlo Control

The off-policy Monte Carlo control algorithm to learn the optimal state-value function $V^*$ is given as follows, which is obtained from Sutton's book. I have three questions concerning this ...
23
votes
2answers
663 views

If machine learning techniques keep improving, what's the role of algorithmics in the future?

Let's look at the future some 30 years from now. Let's be optimistic and assume that areas related to machine learning keep developing as quickly as what we have seen in the past 10 years. That would ...
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votes
1answer
106 views

Supervised learning from “bad” examples - ANN [closed]

I want to recommend one of three possible treatments for a patient, based on his blood values A, B and C. To solve this task, I have constructed a supervised feed-forward NN with back-propagation (...
3
votes
1answer
458 views

Examples of Fat-Shattering Dimension

What are some good examples for analysis of a class's Fat-Shattering dimension? By (Alon et al) I know that the Fat-Shattering Dimension characterizes the learnability of real-valued function classes ...
-1
votes
1answer
269 views

What is the connection between adversarial learning in machine learning and program synthesis?

In particular, I'm considering the similarities in Generative Adversarial Networks and Combinatorial Sketching for Finite Programs. In the first paper, our concern is with learning generator ...
6
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0answers
89 views

Looking for an easy/pedantic exposition of Renegar's famous result on polynomial optimization

In September $1989$, Renegar had this famous sequence of 3 papers titled, "On the Computational Complexity and Geometry of the First-order Theory of the Reals, Part I/II/III". I was wondering if ...
1
vote
0answers
16 views

Can the distribution over the squared moduli of the 'probabilities' defined by an RBM with complex weights be written as an RBM with real weights? [closed]

I posted this question originally on the math boards, but figured it would be better suited here. https://math.stackexchange.com/questions/2203967/can-the-distribution-over-the-squared-moduli-of-the-...
3
votes
1answer
114 views

Minimax agnostic risk for Lipschitz functions

For $L>0$, let $F_L$ be the class of all $L$-Lipschitz functions on $[0,1]$. Let $D$ be a joint distribution on $[0,1]\times\mathbb{R}$, from which we sample $n$ iid copies $(X_i,Y_i)$. Given any $...
4
votes
1answer
176 views

Kleinberg-consistency of spectral clustering

Spectral clustering refers to a family of graph-based algorithms, which usually rely on a similarity function rather than a metric, though a metric $\rho(x,y)$ can always be converted to a similarity ...
2
votes
1answer
104 views

Upper bound on the size of a Concept Lattice (Galois Lattice)?

A context is a tuple $(O, A, R)$ where $O$ is the set of objects, $A$ the set of attributes and $R \subseteq O\times A$ is a relation. For $o \in O$ and $a \in A$ we read $oRa$ as the object $o$ ...
-2
votes
1answer
114 views

Can Pattern Recognition algorithms be considered Oracle Machines (in the Turing sense)?

I was reading Paul Churchland's "Engine of Reason, Seat of the Soul", where argues that humans (and potentially artificial neural networks as well) are capable of non-Turing computation because they ...
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votes
2answers
152 views

Machine Learning: How ML algorithms build classification rules [closed]

I am truly fascinated by algorithms learning on their own with a little help from humans and as a newbie in this field (with programming experience mainly in C/C++), seek your help to obtain the big ...
2
votes
0answers
84 views

How does white noise on the output channels influence the training of a neural network?

Let $\rho(\sigma): \mathbb{R} \rightarrow \mathbb{R}$ be a probability density that is parametrized by a parameter vector $\sigma \in \mathbb{R}^s$ (for example the normal distribution where $s = 1$ ...
1
vote
1answer
88 views

Learning from derivative data

In many machine learning algorithm, it is often assumed that outputs of unknown function and their corresponding inputs are given to estimate the unknown function. However, I wonder whether there ...
50
votes
5answers
5k views

What kind of answer does TCS want to the question “Why do neural networks work so well?”

My Ph.D. is in pure mathematics, and I admit I don't know much (i.e. anything) about theoretical CS. However, I have started exploring non-academic options for my career and in introducing myself to ...
1
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0answers
56 views

A well-known instance of overcomplete dictionaries

sparse representation is: A signal can be represented as a linear combination of basis functions where the set of basis functions is called dictionary and data samples are much more than their ...
5
votes
0answers
246 views

What precisely is the extra power afforded by using deeper nets?

For any choice of activation function (fix the choice for all the hidden nodes for both the following DNNs) do we know of functions which some $k$ (hidden layer) DNN can compute but a $(k-1)-$DNN can'...
0
votes
1answer
344 views

Serial and parallel neural network [closed]

The question is: can exist a parallel or serial neural network or someone talked about this? For explanation, in the network a single record in a data set enter in the input layer as a value between ...
2
votes
2answers
526 views

Convergence and representation theorems for machine learning

I come from a pure math background and am not very familiar with machine learning. So, I'll start with an example to compensate for my confused grasp of the terminology. Let's say we have a function $...
1
vote
1answer
188 views

Are biases necessary to make neural networks universal approximators when using sigmoid activations?

In a neural network, a bias is a constant term that is added to the weighted input in a neuron/unit: output = activation_function( input1*weight1 + ... + inputn*weightn + bias) I can see that the ...
4
votes
0answers
97 views

Adversarial distributions for PAC lower bounds

The various PAC lower bounds (realizable, agnostic, bounded noise) construct distributions supported on $d$ points, where $d$ is the VC-dimension of the hypothesis class in question. Does anyone ...
-2
votes
1answer
66 views

Can you use only the first summation term of cost function for typical logistic regression?

I have recently come across a Matlab implementation that appears to be using only the first term (i.e. in itself a product) of the typical logistic regression cost function. ...
2
votes
0answers
160 views

About lower bounding the sample complexity of a distribution

Given a joint probability distribution over a finite number of random variables (each with a finite range space) of which only a certain subset is observable, is there a notion of "sample complexity" ...
6
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0answers
159 views

Machine learning algorithms on hypergrap models

Graphical models are a very useful tool with many applications, whereby a joint distribution of a set of random variables is modeled using only pairwise dependencies between the variables, and two ...
5
votes
1answer
98 views

Generalization bounds for multiclass learning when the output is vector space?

There are plenty of results for muli-class learning with with fixed discrete labels: $$ \text{Standard multi-class classification:} \begin{cases} f: X \rightarrow Y_{index} = \{1, 2, 3, ..., k \}, \\ ...
0
votes
1answer
72 views

The dependence of learning generalization bounds on the dimension of the instance space

Here is a popular generalization bound: If $X$ is the input space and $Y=\{0, 1\}$ is the output/label space, and there is a joint distribution $D$ defined on this space. We sample $m$ ...
2
votes
0answers
67 views

Impossibility result on metric learning?

Are there any fundamental limitations (impossibility results) known for metric learning? Are there any direct connection reduction from/to that I can use results in clustering? (e.g. this: 2 ) 2 ...
8
votes
1answer
382 views

Competing against an optimal weighted majority in experts algorithm

In the experts problem, $n$ experts give you binary predictions on a daily basis, and you have to predict whether it's going to rain tomorrow. That is, at day $t$, you know the past predictions of ...
2
votes
0answers
54 views

Question about the definition of projecting concepts in learning

I am self-studying in the area of query learning and having a difficulty in understanding the definition of closed under projection for concept classes discussed in several papers (for example, here (...